Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach
This work addresses optimization challenges in computational domains, but it is incremental as it builds upon the classical Differential Evolution algorithm with a hierarchical crossover operation.
The authors tackled the problem of mathematical optimization by introducing a variant of Differential Evolution inspired by the hierarchical and distributed operations of the human motor system, resulting in an algorithm that significantly outperforms standard algorithms and their variants on standard test functions and the CEC 2017 benchmark.
Operational maturity of biological control systems have fuelled the inspiration for a large number of mathematical and logical models for control, automation and optimisation. The human brain represents the most sophisticated control architecture known to us and is a central motivation for several research attempts across various domains. In the present work, we introduce an algorithm for mathematical optimisation that derives its intuition from the hierarchical and distributed operations of the human motor system. The system comprises global leaders, local leaders and an effector population that adapt dynamically to attain global optimisation via a feedback mechanism coupled with the structural hierarchy. The hierarchical system operation is distributed into local control for movement and global controllers that facilitate gross motion and decision making. We present our algorithm as a variant of the classical Differential Evolution algorithm, introducing a hierarchical crossover operation. The discussed approach is tested exhaustively on standard test functions as well as the CEC 2017 benchmark. Our algorithm significantly outperforms various standard algorithms as well as their popular variants as discussed in the results.